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Download Fashion-MNIST

Source: R/fashion-mnist.R
download_fashion_mnist.Rd

Download Fashion-MNIST database of images of fashion products.

Usage

download_fashion_mnist(
  base_url = fashion_mnist_url,
  verbose = FALSE,
  as = c("data.frame", "list"),
  timeout = 1800
)

Format

A data frame with 786 variables:

  • px1, px2, px3 ... px784: Integer pixel value, from 0 (white) to 255 (black).

  • Label: The fashion item represented by the image, in the range 0-9.

  • Description: The name of the fashion item associated with the Label

Pixels are organized row-wise. The Label variable is stored as a factor. The labels correspond to:

  • 0: T-shirt/top

  • 1: Trouser

  • 2: Pullover

  • 3: Dress

  • 4: Coat

  • 5: Sandal

  • 6: Shirt

  • 7: Sneaker

  • 8: Bag

  • 9: Ankle boot

and are also present as the Description factor.

There are 70,000 items in the data set. The first 60,000 are the training set, as found in the train-images-idx3-ubyte.gz file. The remaining 10,000 are the test set, from the t10k-images-idx3-ubyte.gz file.

Items in the dataset can be visualized with the show_mnist_digit() function.

For more information see https://github.com/zalandoresearch/fashion-mnist.

Arguments

base_url

Base URL that the files are located at.

verbose

If TRUE, then download progress will be logged as a message.

as

Return format. Use "data.frame" for the original data frame shape, or "list" for the canonical image result described in download_mnist().

timeout

Minimum download timeout in seconds. The default is 30 minutes; a larger existing global R timeout is preserved.

Value

A data frame containing Fashion-MNIST, or a canonical image result with label and description factors in meta.

Details

Downloads the image and label files for the training and test datasets and converts them to a data frame or canonical image result. The dataset is intended to be a drop-in replacement for the MNIST digits dataset but with more relevance for benchmarking machine learning algorithms (i.e. it's more difficult).

Note

Originally based on a function by Brendan O'Connor.

References

Xiao, H., Kashif, R., & Vollgraf, R. (2017). Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv preprint arXiv:1708.07747. https://github.com/zalandoresearch/fashion-mnist/

Examples

if (FALSE) { # \dontrun{
# download the data set
fashion <- download_fashion_mnist()

# first 60,000 instances are the training set
fashion_train <- head(fashion, 60000)
# the remaining 10,000 are the test set
fashion_test <- tail(fashion, 10000)

# PCA on 1000 examples
fashion_r1000 <- fashion[sample(nrow(fashion), 1000), ]
pca <- prcomp(fashion_r1000[, 1:784], retx = TRUE, rank. = 2)
# plot the scores of the first two components
plot(pca$x[, 1:2], type = "n")
text(pca$x[, 1:2],
  labels = fashion_r1000$Label,
  col = rainbow(length(levels(fashion$Label)))[fashion_r1000$Label]
)
} # }